Application Of Empirical Mode Decomposition Research Articles

Estimating structural and frictional unemployment: An application of empirical mode decomposition

Estimating structural and frictional unemployment: An application of empirical mode decomposition

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

On application of empirical mode decomposition for turbulence analysis in open-channel flows

Large-scale coherent structures are key elements of open-channel flow turbulence, quantification of which remains elusive. In this work, we use empirical mode decomposition (EMD) to break down a velocity time series into different modes, denoted as “intrinsic mode functions” (IMFs). Analysis of velocity auto- and co-spectra indicates that large-scale (LSMs) and very large-scale (VLSMs) fluid motions are sufficiently represented by particular groups of IMFs. A correlation between LSMs and VLSMs, identified by the EMD analysis, was found to generate 7% of the Reynolds shear stresses. However, the EMD analysis of surrogate velocity signals with randomized spectral phases demonstrated that the revealed correlation is actually an artefact of the EMD approach and should not be interpreted physically.

Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows.

Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values ​​were selected as inputs according to partial autocorrelation values ​​in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.

Application of Empirical Mode Decomposition and Hodrick Prescot filter for the prediction single step and multistep significant wave height with LSTM

The study discusses the application of the Empirical Model Decomposition (EMD) and Hodrick Prescot (HP) filter for significant wave height prediction. Long Short-Term Memory (LSTM) is applied for sequence predictions in Natural Language Processing (NLP). LSTM can also be modeled to time series forecasting, particularly for cyclic data since LSTM learns from experience to forecast the data. The time series signal is a combination of frequencies or spectra, when the LSTM is applied to the raw signal the prediction is lagged, to reduce the lag in the forecast the noise in the time series signal is filtered. Since, the time series consists of various frequency components, filtering the higher-frequency components is required to reduce the delay in prediction from the LSTM neural network model. The EMD is applied to the original time series wave data to split the signal into several frequencies in time series or Intrinsic Mode Functions (IMFs). Similarly, when the HP filter is applied to the original wave data or time series signal it gives a trend and cycle. The LSTM is applied to the IMFs and HP filter trend time series to predict the EMD-IMFs and Trend respectively. All the predicted IMFs are summed to compare with the original raw time series. The single step and multistep prediction or forecasting is carried out for 1-h interval, 6-h, 12-h, and 24 h interval. With the increase in forecasting interval the error values also increased and changing the lambda parameter of HP filter played a critical step for the multistep prediction. The EMD-LSTM is computationally expensive as it takes higher amount time to predict each IMF compared to HP-LSTM. Finally, the results are compared with between normal LSTM, EMD-LSTM and HP-LSTM.

Application of empirical mode decomposition for denoising and ground clutter removal on weather radar signals

Proper detection and estimation of signal and noise power measurements are important to generate the best quality of meteorological data products such as Reflectivity, Velocity and Spectrum width. In order to obtain the best quality radar products, it is desirable to compute meteorological parameters by estimating noise power and removal of ground clutter. This paper attempts to study the Empirical Mode Decomposition (EMD) denoising techniques on weather radar signals in the presence of noise and ground clutter. Different methods of EMD based denoising techniques have been considered and applied to the weather signals to check the best performance of the denoising and clutter removal technique. The limitations of these methods are brought out through simulation analysis. To overcome these limitations, this paper proposes a new method for denoising and clutter removal in weather signals. This method is a modified version of the correlation-based EMD Interval Threshold (IT) measurements. Moments have been estimated from these techniques and compared with conventional methods like Pulse pair techniques.

Application of empirical mode decomposition to improve deep learning for US GDP data forecasting

The application of deep learning methods to construct deep neural networks for the prediction of future econometric trends and econometric data has come to receive a lot of research attention. However, it has been found that the long short-term memory (LSTM) model is unstable and overly complex. It also lacks rules for handling econometric data, which can cause errors in prediction and in the actual data. This paper proposes an empirical mode decomposition (EMD) method designed to improve deep learning for understanding US GDP trends and US GDP data prediction research. The US GDP growth rate is used only for LSTM model prediction and for real data comparison; the root mean squared error (RMSE) is 2.7274. The US GDP growth rate is EMD decomposed to obtain the intrinsic mode functions (IMFs) after which the LSTM model is used to predict an RMSE of 0.93557.

Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.

Package AdvEMDpy: Algorithmic Variations of Empirical Mode Decomposition in Python

This paper introduces a new Empirical Mode Decomposition (EMD) Python package called AdvEMDpy that is demonstrably more flexible and which generalises in numerous important ways the existing EMD packages available in Python, R, and MATLAB. The extensions introduced by this AdvEMDpy package both significantly improve the options available to the modeller when applying variations of the EMD methodology as well as also improving the statistical robustness and efficiency of methods otherwise available in existing packages. Unlike many of the functions available for the EMD procedure in existing packages, this EMD package, AdvEMDpy, was developed for customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analysis could be performed. The intrinsic mode functions (IMFs) extracted from various time series contain multi-frequency implicit structures which warrant further study and as such maximum customisability and intricate knowledge of the algorithm and algorithmic variations is required for users to exploit the full power of the EMD approach to time series decomposition. The edge effect is the most familiar and troublesome problem present in EMD. Various techniques, of varying intricacy from numerous works, have been developed, refined, and offered in AdvEMDpy. In addition to edge effects, numerous preprocessing, post-processing, detrended fluctuation analysis techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and algorithmic variations have been incorporated and exposed to the user of AdvEMDpy for fine-tuning of applications of EMD.

A Closer Look at Power-Law Scaling Applied to Sea Surface Temperature from Scripps Pier Using Empirical Mode Decomposition

AbstractThe purpose of this study is to extract more information about the scaling exponents we obtain from sea surface temperature (SST) because their information content is limited to a single value. We examine the application of empirical mode decomposition (EMD) to power-law scaling using SST from Scripps Pier, California. The daily observations we employ extend from 1920 to 2009, a period of 90 years. The annual cycle and the long-term trend were first removed. The decomposition produced a total of 15 modes. The scaling exponents were then calculated separately for each mode from the EMD. We have examined the distribution of scaling exponents with respect to the ensemble, and then with respect to the individual modes for the oceanic processes that we may infer from them. The first three modes are antipersistent and contain about one-quarter of the total variance. The pattern of modes that was obtained is continuous and relatively smooth beyond mode 3 with increasing values up to mode 8 and generally decreasing values thereafter. The pattern exhibits intramodal correlation, as expected, and intermodal correlation as well. Intermodal correlation is likely due, for the most part, to long-range persistence. The annual cycle in SST at Scripps Pier is a dominant feature in the record and contains almost 70% of the variance. A method for removing the annual cycle that is not based on removing the mean value is introduced and is recommended for future use.

Technical Analysis with Empirical Mode Decomposition: A Case in the Hong Kong Stock Market

This article studies the performance improvement of technical analysis with the application of empirical mode decomposition. The study employs data from the Hong Kong stock market and addresses issues arising from gaps in intraday data. The results support the use of empirical mode decomposition in technical analysis to improve performance. <b>TOPICS:</b>Technical analysis, emerging markets, statistical methods, performance measurement <b>Key Findings</b> ▪ Empirical mode decomposition improves technical analysis profitability via denoising before candlestick construction. ▪ Empirical results show 68 basis points of profitability improvement a year on average in the Hong Kong stock market. ▪ Empirical results are robust regardless of the methods to handle the lunch gap in the data.

An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing

Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. The most important part of this improved method is to change the mean value by envelopes of signal in EMD to the mean value by the definite integral, which enables the mean value to be mathematically expressed strictly. Firstly, we prove that the signal is orthogonally decomposed by the improved method. Secondly, the Monte Carlo method of white noise is used to explain that the improved method can effectively alleviate mode-mixing. In addition, the improved method is adaptive and does not need any input parameters, and the intrinsic mode functions (IMFs) generated from it is robust to sifting. We have carried out experiments on a series of artificial and real data, the results show that the improved method is the orthogonal decomposition method and can effectively alleviate mode-mixing, and it has better decomposition performance and physical meaning than EMD, ensemble EMD (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In addition, the improved method is generally more time-consuming than EMD, but far less than EEMD and CEEMDAN.

Identification of wave impacts and separation of responses using EMD

Various marine and off-shore structures experience rare extreme, and frequent substantial wave impacts, and these impacts have significant implications respectively for the ultimate strength and fatigue life of these structures. Likewise, many other structures experience forms of impact among their environmental loads that are critical for their design. One way of exploring these impacts is to study the vibration signal of the structure. However, due to the complexity of the non-stationary signal, the wave impacts on marine and off-shore structures are difficult to analyse. Using signals acquired during trials of a high-speed catamaran as a case study, this paper proposes employing the Empirical Mode Decomposition (EMD) to detect, identify and characterise significant wave impacts (known as ‘slams’), which is shown to be significantly better at classifying events than traditional methods of slam detection. With the application of EMD, the vibration signal is decomposed into many components that may be grouped into three categories: (1) the rigid-body response comprising two parts: the quasi-static response to the underlying wave spectrum (hydrostatic) and rigid-body resonance (hydrodynamic); (2) the elastic structural response (hydroelastic); and (3) local high frequency vibrations and/or noise. It is the second category that responds significantly to wave impact loads. By identifying the gap between the rigid body and structural resonant frequencies, a threshold is established to automate the physically rational separation of this hydroelastic response. Wave impact detection methods are then applied to this separated component, showing 94% true positive classification, compared with 64% for a recently published slam detection method that uses traditional filtering applied to the whole signal. Further, it allows for more targeted subsequent characterisation of the impact response. Thus, this paper concludes that EMD is an effective method to detect and identify wave impacts.

Application of Ensemble Empirical Mode Decomposition in Low-Frequency Lightning Electric Field Signal Analysis and Lightning Location

The application of empirical mode decomposition (EMD) in the analysis and processing of lightning electric field waveforms acquired by the low-frequency e-field detection array (LFEDA) in China has significantly improved the capabilities of the low-frequency/very-low-frequency (LF/VLF) time-of-arrival technique for studying the lightning discharge processes. However, the inherent mode mixing and the endpoint effect of EMD lead to certain problems, such as an inadequate noise reduction capability, the incorrect matching of multistation waveforms, and the inaccurate extraction of pulse information, which limit the further development of the LFEDA’s positioning ability. To solve these problems, the advanced ensemble EMD (EEMD) technique is introduced into the analysis of LF/VLF lightning measurements, and a double-sided bidirectional mirror (DBM) extension method is proposed to overcome the endpoint effect of EMD. EEMD can effectively suppress mode mixing, and the DBM extension method proposed in this article can effectively suppress the endpoint effect, thus greatly improving the accuracy of a simulated signal after a 25–500-kHz bandpass filter. The resulting DBM_EEMD algorithm can be used in the LFEDA system to process and analyze the detected electric field signals to improve the system’s lightning location capabilities, especially in terms of accurate extraction and location of weak signals from lightning discharges. In this article, a 3-D image of artificially triggered lightning obtained from an LF/VLF location system is reported for the first time, and methods for further improving the location capabilities of the LF/VLF lightning detection systems are discussed.

Detection and Experimental Validation of High Impedance Arc Fault in Distribution System Using Empirical Mode Decomposition

The effect of high impedance faults (HIFs) in a distribution system has an effect on the system stability and reliability. The high impedance arc fault (HIAF) even poses a significant threat to the living being as it involves arcing. The HIAF have the same V–I characteristics as HIF, but the enormous amount of heat generation is a significant concern to differentiate between them. Moreover, there are different types of arc which may occur depending on the arcing conditions and arcing surfaces. In this article, arc due to leaning-tree on a medium-voltage distribution line and the arc in between sphere-gaps are considered as the main event of analysis. An empirical mode decomposition (EMD)-based approach is used along with artificial neural network (ANN) for the analysis of real-time arc signals. The result obtained by the application of EMD and ANN on arc voltage signals successfully detects and classifies the arcing events by their predominant harmonic signatures. A comparison of EMD results with other techniques is presented. Arc in soil and wet-sand is also considered for the validation of the proposed algorithm for other arcing surfaces. Support vector machine and K th nearest neighbor machine learning techniques are also utilized for classification.

Andritz Group, Austria

Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and is therefore subject to faults, which could be detected and diagnosed by using signal processing techniques. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been extensively studied and widely applied in fault diagnosis of rotating machinery. Numerous publications on the use of EMD for fault diagnosis have appeared in academic journals, conference proceedings and technical reports. This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics. First, the EMD method is briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of EMD to fault diagnosis of rotating machinery are summarized in terms of the key components, such as rolling element bearings, gears and rotors. Finally, the outstanding open problems of EMD in fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction of EMD for those new to the concepts, as well as a summary of the current frontiers of its applications to fault diagnosis for experienced researchers.

Application of Empirical mode Decomposition with Wavelets Support Vector Machine in time Series Data

This paper mainly discussed on the forecast of Thailand tourist visiting Malaysia. This paper proposed a three-stage technique in which the empirical mode decomposition (EMD) is combined with wavelet methods and support vector machine model. We used the proposed technique, EMD_WSVM to forecast two ASEAN country tourism timeseries. Detail experiments are conducted for the proposed method, in which there is a comparison between the EMD_WSVM, WSVM and SVM methods. The proposed EMD_WSVM model is determined to be dominant to the other methods in predicting the number of tourist arrivals.

Application of Empirical Mode Decomposition and Extension Neural Network Type-3 to Feature Diagnosis of Electrocardiograms

Application of Empirical Mode Decomposition and Extension Neural Network Type-3 to Feature Diagnosis of Electrocardiograms

Application of EMD and 1.5‐dimensional spectrum in fault feature extraction of rolling bearing

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA () outperforms VRA () for moderate to high noise levels whereas mVRA outperformed EMD () for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (. For Raman spectra of synthetic material, good performance of the three methods ( for VRA, for EMD, and for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ( for VRA, for EMD, and for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.